improved-gan
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openai
question of implementation of inception score
these lines puzzle me:
w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
logits = tf.matmul(tf.squeeze(pool3, [1, 2]), w)
softmax = tf.nn.softmax(logits)
I'm wondering, why not just use sess.graph.get_tensor_by_name('softmax:0') ? Why bother to manually do the matrix multiplication and apply softmax? also, why not add the bias term?
ok , I know that inception model require the batch size to be 1 if we just run sess.graph.get_tensor_by_name('softmax:0'). (the restriction comes from tf.get_default_graph().get_tensor_by_name('pool_3/_reshape/shape_1:0') which has the fixed value of [1, 2048]. but still, I can't understand why not add the bias term
without the bias term ( as in the repo), I get 10.954855 +- 0.4320521 inception score on CIFAR-10(using test images).
with the bias term ( as in the repo), I get 11.228305 +- 0.45700935 inception score on CIFAR-10(using test images).
so I think that the bias term should be added. It matters.
I also found this issue. I actually tested the inception score with bias term and without inception score with a set of experiments: the basic founding is that inception score with bias term is consistently higher than the one without bias term, with a relatively fixed gap.
It does matter a lot, but the official implementation does not take it into account, so maybe most other reported inception scores also do not. I choose to report the score without the bias term to provide a fair comparison.
I think it is indeed important to know whether there has consideration on keep or drop the bias term from the original author.
-- Zhiming Zhou
On Thu, Jun 28, 2018 at 4:20 PM, youkaichao [email protected] wrote:
without the bias term ( as in the repo), I get 10.954855 +- 0.4320521 inception score on CIFAR-10(using test images).
with the bias term ( as in the repo), I get 11.228305 +- 0.45700935 inception score on CIFAR-10(using test images).
so I think that the bias term should be added. It matters.
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without the bias term ( as in the repo), I get 10.954855 +- 0.4320521 inception score on CIFAR-10(using test images).
with the bias term ( as in the repo), I get 11.228305 +- 0.45700935 inception score on CIFAR-10(using test images).
so I think that the bias term should be added. It matters.
Hello, I am currently working on a GAN-related topic involving the calculation of the inception score, but when I use the source code of the author to run the real data of CIFAR10, the inception score is only (5.5425735, 0.059681736) (train data) & (5.5588408, 0.17018904) (test data), and I also used the pytorch version of the code for evaluation, the result is at 9.5+, this problem has been bothering me for a few days, and also checked a lot of information on the network, including issues There is no mention of the relevant details. So I am very confused. So I would like to ask you how to get the result of 11.24 in paper? Can you share your code or give me a hint? Thank you very much!
@Adherer I think you can take a look at here. But it seems that the model file of that link has changed. Fortunately, I have saved a copy of that model file at here. (It annoys me that the link still works but actually points to another model file)
@Adherer I think you can take a look at here. But it seems that the model file of that link has changed. Fortunately, I have saved a copy of that model file at here. (It annoys me that the link still works but actually points to another model file)
I use your method,and get the code from that website.But it still did not work,and get this error:
Traceback (most recent call last):
File "inception_score.py", line 156, in